Enhancing Recommender System with Linked Open Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10139475" target="_blank" >RIV/00216208:11320/13:10139475 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-642-40769-7_42" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-642-40769-7_42</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-40769-7_42" target="_blank" >10.1007/978-3-642-40769-7_42</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing Recommender System with Linked Open Data

Popis výsledku v původním jazyce

In this paper, we present an innovative method to use Linked Open Data (LOD) to improve content based recommender systems. We have selected the domain of secondhand bookshops, where recommending is extraordinary difficult because of high ratio of objects/users, lack of significant attributes and small number of the same items in stock. Those difficulties prevents us from successfully apply both collaborative and common content based recommenders. We have queried Czech language mutation of DBPedia in order to receive additional attributes of objects (books) to reveal nontrivial connections between them. Our approach is general and can be applied on other domains as well. Experiments show that enhancing recommender system with LOD can significantly improve its results in terms of object similarity computation and top-k objects recommendation. The main drawback hindering widespread of such systems is probably missing data about considerable portion of objects, which can however vary acros

Název v anglickém jazyce

Enhancing Recommender System with Linked Open Data

Popis výsledku anglicky

In this paper, we present an innovative method to use Linked Open Data (LOD) to improve content based recommender systems. We have selected the domain of secondhand bookshops, where recommending is extraordinary difficult because of high ratio of objects/users, lack of significant attributes and small number of the same items in stock. Those difficulties prevents us from successfully apply both collaborative and common content based recommenders. We have queried Czech language mutation of DBPedia in order to receive additional attributes of objects (books) to reveal nontrivial connections between them. Our approach is general and can be applied on other domains as well. Experiments show that enhancing recommender system with LOD can significantly improve its results in terms of object similarity computation and top-k objects recommendation. The main drawback hindering widespread of such systems is probably missing data about considerable portion of objects, which can however vary acros

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science

ISBN

978-3-642-40768-0

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

12

Strana od-do

483-494

Název nakladatele

Springer Berlin / Heidelberg

Místo vydání

Heidelberg

Místo konání akce

Granada, Spain

Datum konání akce

18. 9. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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